from joblib import load
import numpy as np

class ClassIfication(object):
    
    def __init__(self):
        self.knn = load('knn_classifien.joblib')
        self.scaler = load('feature_scaler.joblib')
        self.le = load('label_encoder.joblie')
        
    def get_predicter_category(self, new_data):
        """获取预测结果
        """
        # 转换为数组并保存特征顺序
        feature_order =['eps', 'total_revenue_ps', 'undist_profit_ps', 'gross_margin', 'fcff', 'fcfe', 'tangible_asset', 'bps', 'grossprofit_margin', 'npta', 'roic']
        new_values = np.array([[new_data[col] for col in feature_order]])
        # 标准化新数据
        new_scaled = self.scaler.transform(new_values)
        
        # 预测分类
        predicted_label = self.knn.predict(new_scaled)
        predicted_category = self.le.inverse_transform(predicted_label)
        return predicted_category[0]
        
if __name__ == '__main__':
    ci = ClassIfication()
    new_data = {
        'eps': "0.84", 
        'total_revenue_ps': "16.8383", 
        'undist_profit_ps': "8.3656", 
        'gross_margin': "37901400000", 
        'fcff': "15423200000", 
        'fcfe': "19577000000", 
        'tangible_asset': "186107000000", 
        'bps': "20.8979", 
        'grossprofit_margin': "18.8665", 
        'npta': "0.8815", 
        'roic': "2.204"
    }
    predicted_category = ci.get_predicter_category(new_data)
    print(predicted_category)